A Genre-based Clustering Approach to Content Extraction

The content of a webpage is usually contained within a small body of text and images, or perhaps several articles on the same page; however, the content may be lost in the clutter (defined as cosmetic features such as animations, menus, sidebars, obtrusive banners). Automatic content extraction has many applications, including browsing on small cell phone and PDA screens, speech rendering for the visually impaired, and reducing noise for information retrieval systems. We have developed a framework, Crunch, which employs various heuristics for content extraction in the form of filters applied to the webpage's DOM tree; the filters aim to prune or transform the clutter, leaving only the content. Crunch allows users to tune what we call 'settings', consisting of thresholds for applying a particular filter and/or for toggling a filter on/off, because the HTML components that characterize clutter can vary significantly from website to website. However, we have found that the same settings tend to work well across different websites of the same genre, e.g., news or shopping, since the designers often employ similar page layouts. In particular, Crunch could obtain the settings for a previously unknown website by automatically classifying it as sufficiently similar to a cluster of known websites with previously adjusted settings. We present our approach to clustering a large corpus of websites into genres, using their pre-extraction textual material augmented by the snippets generated by searching for the website's domain name in web search engines. Including these snippets increases the frequency of function words needed for clustering. We use existing Manhattan distance measure and hierarchical clustering techniques, with some modifications, to pre-classify the corpus into genres offline. Our method does not require prior knowledge of the set of genres that websites fit into, but to be useful a priori settings must be available for some member of each cluster or a nearby cluster (otherwise defaults are used). Crunch classifies newly encountered websites online in linear-time, and then applies the corresponding filter settings, with no noticeable delay added by our content-extracting web proxy

A Genre-based Clustering Approach to Content Extraction

The content of a webpage is usually contained within a small body of text and images, or perhaps several articles on the same page; however, the content may be lost in the clutter (defined as cosmetic features such as animations, menus, sidebars, obtrusive banners). Automatic content extraction has many applications, including browsing on small cell phone and PDA screens, speech rendering for the visually impaired, and reducing noise for information retrieval systems. We have developed a framework, Crunch, which employs various heuristics for content extraction in the form of filters applied to the webpage's DOM tree; the filters aim to prune or transform the clutter, leaving only the content. Crunch allows users to tune what we call 'settings', consisting of thresholds for applying a particular filter and/or for toggling a filter on/off, because the HTML components that characterize clutter can vary significantly from website to website. However, we have found that the same settings tend to work well across different websites of the same genre, e.g., news or shopping, since the designers often employ similar page layouts. In particular, Crunch could obtain the settings for a previously unknown website by automatically classifying it as sufficiently similar to a cluster of known websites with previously adjusted settings. We present our approach to clustering a large corpus of websites into genres, using their pre-extraction textual material augmented by the snippets generated by searching for the website's domain name in web search engines. Including these snippets increases the frequency of function words needed for clustering. We use existing Manhattan distance measure and hierarchical clustering techniques, with some modifications, to pre-classify the corpus into genres offline. Our method does not require prior knowledge of the set of genres that websites fit into, but to be useful a priori settings must be available for some member of each cluster or a nearby cluster (otherwise defaults are used). Crunch classifies newly encountered websites online in linear-time, and then applies the corresponding filter settings, with no noticeable delay added by our content-extracting web proxy

Genre Classification of Websites Using Search Engine Snippets

Web pages often contain clutter (such as ads, unnecessary images and extraneous links) around the body of an article, which distracts a user from actual content. Automatic extraction of 'useful and relevant' content from web pages has many applications, including browsing on small cell phone and PDA screens, speech rendering for the visually impaired, and reducing noise for information retrieval systems. Prior work has led to the development of Crunch, a framework which employs various heuristics in the form of filters and filter settings for content extraction. Crunch allows users to tune these settings, essentially the thresholds for applying each filter. However, in order to reduce human involvement in selecting these heuristic settings, we have extended this work to utilize a website's classification, defined by its genre and physical layout. In particular, Crunch would then obtain the settings for a previously unknown website by automatically classifying it as sufficiently similar to a cluster of known websites with previously adjusted settings - which in practice produces better content extraction results than a single one-size-fits-all set of setting defaults. In this paper, we present our approach to clustering a large corpus of websites by their genre, utilizing the snippets generated by sending the website's domain name to search engines as well as the website's own text. We find that exploiting these snippets not only increased the frequency of function words that directly assist in detecting the genre of a website, but also allow for easier clustering of websites. We use existing techniques like Manhattan distance measure and Hierarchical clustering, with some modifications, to pre-classify websites into genres. Our clustering method does not require prior knowledge of the set of genres that websites fit into, but instead discovers these relationships among websites. Subsequently, we are able to classify newly encountered websites in linear-time, and then apply the corresponding filter settings, with no noticeable delay introduced for the content-extracting web proxy

Analisis Isi Buku Sekolah Elektronik (Bse) Biologi Kelas XI Semester 1 Berdasarkan Literasi Sains

This research aimed to provide information about the scope of science literacy in Buku Sekolah Elektronik (BSE) or electronic school books of biology for grade XI in Tangerang Selatan. The instrument used to analyze the text of courses on biology books, which were taken by multistage sampling technique, was scientific literacy sheet categories adopted from John Wilkinson previously developed by Chiappetta, Fillman, and Sethna. The scientific literacy categories used in this research: science as a body of knowledge; science as a way of investigating; science as thinking; and interaction between science, technology, and society. Agreement of two observers was implemented to ensure the level of reliability of the findings analysis which later determined as coefficient of agreement (Koefisien Kesepakatan/KK). Results showed that the scope of scientific literacy of the analyzed BSE emphasize on the category of science as the body of knowledge (82.7%) and the lowest was the category of interaction between science, technology and society (0.8%)

Determinants of translation efficiency and accuracy

A given protein sequence can be encoded by an astronomical number of alternative nucleotide sequences. Recent research has revealed that this flexibility provides evolution with multiple ways to tune the efficiency and fidelity of protein translation and folding

Nonlinear harmonic generation and devices in doubly-resonant Kerr cavities

We describea theoretical analysis of the nonlinear dynamics of third-harmonic generation ($\omega\to3\omega$) via Kerr (\chithree) nonlinearities in a resonant cavity with resonances at both $\omega$ and $3\omega$. Such a doubly resonant cavity greatly reduces the required power for efficient harmonic generation, by a factor of $\sim V/Q^2$ where $V$ is the modal volume and $Q$ is the lifetime, and can even exhibit 100% harmonic conversion efficiency at a critical input power. However, we show that it also exhibits a rich variety of nonlinear dynamics, such as multistable solutions and long-period limit cycles.We describe how to compensate for self/cross-phase modulation (which otherwise shifts the cavity frequencies out of resonance), and how to excite the different stable solutions (and especially the high-efficiency solutions) by specially modulated input pulses.Comment: 12 pages, 12 figure

The Best and Worst of Contracts Decisions: An Anthology

Five hundred years ago, the common law of contract was without substance. It was form-procedure. Plaintiffs picked a form of action, and common law judges made sure someone besides themselves answered all the hard questions; the parties, a jury, or a ritual determined the winner and the remedy. Judges ran a switch on a conflicts-resolution railway. Thomas More, when Chancellor of England (1529-33), urged judges to lay tracks and control the trains. The problem, he said, was that the judges, by the verdict of the jury[,] cast off all quarrels from themselves. The judges soon assumed greater authority, taking responsibility for the law\u27s substance. The consideration requirement was in place by 1539, and judges afterwards imposed doctrine upon doctrine. Over centuries, they created the common law of contract. That law is now mature, more or less, meaning that judges have tools to fix what they want to fix, and feel free to do so. The law they created-the common law of contract-is a remarkable intellectual and political achievement